Redundancy removal feature selection method LLRFC score+ based on LLRFC and correlation analysis

Abstract

The invention provides a redundancy removal feature selection method LLRFC (Locally Linear Representation Fisher Criterion) score+ based on LLRFC and correlation analysis. A DNA (Deoxyribonucleic Acid) microarray technology provides a new direction for clinic tumor diagnosis. Performance of gene expression data corresponding to different kinds of tumor is different; through the analysis on the tumor gene expression data, study personnel can realize the accurate recognition on the tumor and the tumor subtype in the molecular level; and important biological significance is realized on the diagnosis and the treatment of the tumor. The feature genes in LLRFC judging criterion descending sort gene expression data is used to be combined with the dynamic correlation analysis strategy for further eliminating redundant features; an LLRFC score+ algorithm is provided; and the optimum feature gene subset is selected. The feature selection method LLRFC score+ has the advantages that the classification precision of a classifier can be effectively improved; a sample data set does not need to meet the normal distribution; and the method is applicable to data in various distribution types. The feature selection method LLRFC score+ can help people to find the virulence gene of cancer, and the early-stage diagnosis, tumor staging and typing, prognosis treatment and the like of clinic tumor diseases are facilitated.

Description

Technical field [0001] The invention relates to the technical field of tumor classification research of bioinformatics, and is a feature selection method for tumor gene expression profile data. Background technique [0002] In recent years, the development of gene chip technology has made it possible to detect the expression levels of thousands of genes in parallel on a large scale, opening up a new way for the diagnosis and prevention of human diseases from the molecular biology level. By analyzing the differences in gene expression in different tissue types (such as normal cells and tumor cells or different stages of cancer), and classifying the corresponding gene expression data, clinical diagnosis and treatment of tumor diseases, subtype identification and prognosis analysis are realized. At present, the morbidity and mortality of cancer patients have been on the rise, and they have become the number one killer of human health. Therefore, the use of gene chip technology to st...

AI Technical Summary

Problems solved by technology

The features extracted by LLRFC have no clear biological meaning and are not very explanatory

And due to the complexity of gene expression data, the LLRFC algorithm does not consider the interrelationships between eigengenes, and there is still redundancy in the selected eigengenes.

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